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Academic Performance Index Regression Analysis

            In 1999, the State of California implemented a metric system to monitor public school progress .
             and to ensure full compliance with the Federal No Child Left Behind Act. This metric, the Academic .
             Performance Index (referred to as API), has become one of the most looked to measures of .
             school success, but has also become a large target of criticism. Arguably, this measure is clouded .
             by a number of factors that leave students and teachers disadvantaged, inhibiting their .
             performance, further discouraging them, and ultimately inhibiting their performance even more. .
             Our motivation in examining API is to better understand the factors that best predict this .
             score, and separate out any predictive variables that are strongly correlated to the outcome. .
             Through this statistical analyses, we intend to better understand what schools can focus on .
             offering students (and families) to counteract a socioeconomic and demographic sensitive metric, .
             and improve performance across the board. .
             Through our analyses, we have concluded that a school's API is largely explained by its .
             students' socioeconomic status and demographic features. Through the creation of these models, .
             we can better predict which schools will benefit from assistance (through refocused funding and .
             resources), and help the State reach its goals of academic performance and progress. .
             Introduction and Data Cleaning.
             In 1999, as part of the Public Schools Accountability Act, the California State Legislature directed the Department of Education to develop a quantitative metric for measuring school progress and performance. The Academic Performance Index (API), based primarily on state standardized testing scores (CST) and high school exit exam scores (CAHSEE). This index ranges from 200 to 1000. Especially in light of recent fiscal woes, schools are looking to maximize this value to justify their funding. Unfortunately, as with any quantitative measure of performance, many claim this value is ineffective and biased and argue against using it to hold schools accountable.

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